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A data driven robust optimization model for scheduling near-zero carbon emission power plant considering the wind power output uncertainties and electricity-carbon market

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  • Li, Yanbin
  • Sun, Yanting
  • Liu, Jiechao
  • Liu, Chang
  • Zhang, Feng

Abstract

Under the carbon peak and neutrality targets, the power generation industry in China is facing an urgent demand for a low carbon transition and participation in the electricity-carbon market. This paper novelty proposes a near-zero carbon emission power plant (NZCEP) integrating gas turbines, wind turbines, power-to-gas and the carbon capture, utilization and storage system. And a two-stage Data-driven Set based robust optimization (DSRO) model, including a day-ahead dispatching phase and a real-time adjustment phase, is conducted to ensure the consumption of renewable energy resources and to develop the optimal operating strategy for NZCEP participation under the electricity-carbon market. The results demonstrated the following outcomes: (1) NZCEP shows better environmental benefits by reducing carbon emissions and consuming renewable energy resources. (2) the DSRO model can resist the interference of wind power output uncertainties and address the issue of traditional robust models being overly conservative. (3) under the electricity-carbon market, NZCEP shows better economic benefits, which can generate additional revenue from selling surplus carbon emission allowances (CEA). Moreover, when the annual average CEA price reaches 355 CNY/t, the NZCEP will achieve full capital cost recovery.

Suggested Citation

  • Li, Yanbin & Sun, Yanting & Liu, Jiechao & Liu, Chang & Zhang, Feng, 2023. "A data driven robust optimization model for scheduling near-zero carbon emission power plant considering the wind power output uncertainties and electricity-carbon market," Energy, Elsevier, vol. 279(C).
    • Handle: RePEc:eee:energy:v:279:y:2023:i:c:s0360544223014470
    DOI: 10.1016/j.energy.2023.128053
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    2. Zhao, Xudong & Wang, Yibo & Liu, Chuang & Cai, Guowei & Ge, Weichun & Wang, Bowen & Wang, Dongzhe & Shang, Jingru & Zhao, Yiru, 2024. "Two-stage day-ahead and intra-day scheduling considering electric arc furnace control and wind power modal decomposition," Energy, Elsevier, vol. 302(C).
    3. Huang, Chu & Zhu, Haixi & Ma, Yinjie & E, Jiaqiang, 2023. "Evaluation of lithium battery immersion thermal management using a novel pentaerythritol ester coolant," Energy, Elsevier, vol. 284(C).
    4. Seyed Mohammad Shojaei & Reihaneh Aghamolaei & Mohammad Reza Ghaani, 2024. "Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review," Sustainability, MDPI, vol. 16(21), pages 1-41, November.
    5. Shen, Haotian & Zhang, Hualiang & Xu, Yujie & Chen, Haisheng & Zhang, Zhilai & Li, Wenkai & Su, Xu & Xu, Yalin & Zhu, Yilin, 2024. "Two stage robust economic dispatching of microgrid considering uncertainty of wind, solar and electricity load along with carbon emission predicted by neural network model," Energy, Elsevier, vol. 300(C).
    6. Zhou, Yanting & Ma, Zhongjing & Shi, Xingyu & Zou, Suli, 2024. "Multi-agent optimal scheduling for integrated energy system considering the global carbon emission constraint," Energy, Elsevier, vol. 288(C).
    7. Sicheng Wang & Weiqing Sun, 2023. "Capacity Value Assessment for a Combined Power Plant System of New Energy and Energy Storage Based on Robust Scheduling Rules," Sustainability, MDPI, vol. 15(21), pages 1-19, October.

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